{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0065a29e-b528-40cb-9b14-5eeb94c98e84",
   "metadata": {},
   "source": [
    "# Using MLflow\n",
    "> Log your neuralforecast experiments to MLflow"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4fb1958",
   "metadata": {},
   "source": [
    "## Installing dependencies"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "963311a3-2427-4694-981b-438fce1c2981",
   "metadata": {},
   "source": [
    "To install Neuralforecast refer to [Installation](../getting-started/04_installation).\n",
    "\n",
    "To install mlflow: `pip install mlflow`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1125a0bc",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "687f6677",
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import warnings\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import mlflow\n",
    "import mlflow.data\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from mlflow.client import MlflowClient\n",
    "from mlflow.data.pandas_dataset import PandasDataset\n",
    "from utilsforecast.plotting import plot_series\n",
    "\n",
    "from neuralforecast.core import NeuralForecast\n",
    "from neuralforecast.models import NBEATSx\n",
    "from neuralforecast.utils import AirPassengersDF\n",
    "from neuralforecast.losses.pytorch import MAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dbafb920-4af8-49d0-abfa-2ff79f2cfd00",
   "metadata": {},
   "outputs": [],
   "source": [
    "logging.getLogger(\"mlflow\").setLevel(logging.ERROR)\n",
    "logging.getLogger(\"pytorch_lightning\").setLevel(logging.ERROR)\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2570db3c-d8f7-4b07-bd43-d79d730b07cb",
   "metadata": {},
   "source": [
    "## Splitting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d556dc4-8965-4dfd-809a-7b87d348d5ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1960-08-31</td>\n",
       "      <td>606.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1960-09-30</td>\n",
       "      <td>508.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1960-10-31</td>\n",
       "      <td>461.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1960-11-30</td>\n",
       "      <td>390.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1960-12-31</td>\n",
       "      <td>432.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     unique_id         ds      y\n",
       "139        1.0 1960-08-31  606.0\n",
       "140        1.0 1960-09-30  508.0\n",
       "141        1.0 1960-10-31  461.0\n",
       "142        1.0 1960-11-30  390.0\n",
       "143        1.0 1960-12-31  432.0"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split data and declare panel dataset\n",
    "Y_df = AirPassengersDF\n",
    "Y_train_df = Y_df[Y_df.ds<='1959-12-31'] # 132 train\n",
    "Y_test_df = Y_df[Y_df.ds>'1959-12-31'] # 12 test\n",
    "Y_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "919711ee-6fe1-442b-a11f-6031cd4b4999",
   "metadata": {},
   "source": [
    "## MLflow UI\n",
    "Run the following command from the terminal to start the UI: `mlflow ui`. You can then go to the printed URL to visualize the experiments."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00768993-bcf7-43d5-9ac3-2f916e52acc6",
   "metadata": {},
   "source": [
    "## Model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4467763",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Seed set to 42\n"
     ]
    }
   ],
   "source": [
    "mlflow.pytorch.autolog(checkpoint=False)\n",
    "\n",
    "with mlflow.start_run() as run:\n",
    "    # Log the dataset to the MLflow Run. Specify the \"training\" context to indicate that the\n",
    "    # dataset is used for model training\n",
    "    dataset: PandasDataset = mlflow.data.from_pandas(Y_df, source=\"AirPassengersDF\")\n",
    "    mlflow.log_input(dataset, context=\"training\")\n",
    "\n",
    "    # Define and log parameters\n",
    "    horizon = len(Y_test_df)\n",
    "    model_params = dict(\n",
    "        input_size=1 * horizon,\n",
    "        h=horizon,\n",
    "        max_steps=300,  \n",
    "        loss=MAE(),\n",
    "        valid_loss=MAE(),  \n",
    "        activation='ReLU',\n",
    "        scaler_type='robust',\n",
    "        random_seed=42,\n",
    "        enable_progress_bar=False,\n",
    "    )\n",
    "    mlflow.log_params(model_params)\n",
    "\n",
    "    # Fit NBEATSx model\n",
    "    models = [NBEATSx(**model_params)]\n",
    "    nf = NeuralForecast(models=models, freq='M')           \n",
    "    train = nf.fit(df=Y_train_df, val_size=horizon)\n",
    "    \n",
    "    # Save conda environment used to run the model (if you used a conda environment)\n",
    "    mlflow.pytorch.get_default_conda_env()\n",
    "    \n",
    "    # Save pip requirements\n",
    "    mlflow.pytorch.get_default_pip_requirements()\n",
    "\n",
    "mlflow.pytorch.autolog(disable=True)\n",
    "\n",
    "# Save the neural forecast model\n",
    "nf.save(path='./checkpoints/test_run_1/',\n",
    "        model_index=None, \n",
    "        overwrite=True,\n",
    "        save_dataset=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f72c67d-8d93-4a93-aa15-632c1ca3ae00",
   "metadata": {},
   "source": [
    "## Forecasting the future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62bec1b2-c07f-4ffd-8e2f-d2581ec3fcf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x350 with 1 Axes>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_hat_df = nf.predict(futr_df=Y_test_df)\n",
    "plot_series(Y_train_df, Y_hat_df, palette='tab20b')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
